Meta-Analysis of Artificial Intelligence in Education
Bibliographic record
Abstract
This meta-analysis examined the effectiveness of artificial intelligence (AI) technologies in educational settings through a systematic review of 13 empirical studies conducted across eight countries. We analysed the impact of various AI technologies on educational outcomes using PRISMA guidelines and multiple analytical approaches, including novel applications of Naive Bayes, TF-IDF, and BERT-based algorithms. The overall analysis revealed a significant positive effect size (Hedges' g = 0.86, 95% CI [0.45, 1.27], p < 0.0001), indicating substantial benefits of AI integration in education. Particularly noteworthy were the effects of chatbots and generative AI (effect size = 1.02, 95% CI [0.45, 1.59], p < 0.0001), which demonstrated the most substantial positive impact on student learning outcomes. Online learning and virtual reality applications showed moderate positive effects (effect size = 0.79, 95% CI [-0.04, 1.62], p < 0.07) while learning management systems and AI platforms demonstrated promising but more modest impacts (effect size = 0.62, 95% CI [0.03, 1.21], p < 0.05). Although significant heterogeneity was observed across studies (I² ranging from 54.03% to 93.23%), the consistent positive effects across different educational contexts suggest the robust potential of AI technologies in enhancing educational practices. Implementing a novel weighted hybrid model, combining random and fixed effects approaches, provided additional methodological insights for analysing educational technology effectiveness. These findings provide empirical support for integrating AI technologies in educational settings while highlighting the importance of considering specific contextual factors and implementation strategies for optimal outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".